Steiner Tree

The Steiner tree problem seeks the minimum-cost network connecting a set of specified points, potentially including additional intermediary points (Steiner points) to reduce overall cost. Current research focuses on developing efficient approximation algorithms, often leveraging graph neural networks (GNNs) and reinforcement learning (RL) to predict optimal Steiner points or learn effective heuristics for finding near-optimal solutions. These advancements are crucial for various applications, including VLSI design, network optimization, and data analysis, where finding efficient solutions to this NP-hard problem is critical for performance and resource management. The development of robust and scalable methods for solving Steiner tree problems continues to be a significant area of investigation.